Texture-based Authentication of Digital Identity Documents

ABSTRACT

Techniques for texture-based authentication of digital identity documents are disclosed. The disclosed techniques include authentication based on global texture information and/or on local texture information. Global texture-based authentication may include generating a global texture profile for an identity document image and comparing the global texture profile with a stored profile associated with a jurisdiction class of the identity document. Local texture-based authentication may include generating one or more local texture patches representative of texture information of one or more select local blocks of the ID. The one or more local texture patches are provided as input to one or more local detectors each trained to detect the presence of a forgery based on a target manipulation space.

FIELD

The invention relates generally to authentication of identity documents,and more particularly to the authentication of digital identitydocuments using texture information.

BACKGROUND

Today's increasing demand for online and mobile verification of identitydocuments has created a strong need for authentication solutions withvarious fraud detection capabilities.

Existing systems can generally be divided between active and passivesolutions.

Active solutions rely on embedding anti-counterfeiting security featuressuch as watermarks, linecodes, etc. in an identity document to increasethe difficulty of tampering with the document. Such solutions howevermay not be effective at detecting tampering that does not affect theembedded security features.

Passive solutions are based on checking whether an identity documentcontains traces of forgery or manipulation in any portion of thedocument. Typically, these solutions use pixel-level analysis toidentify predetermined tampering operations, and generally focus onchecking manipulations around personally identifiable information (PII)areas only.

BRIEF SUMMARY

Systems and methods for texture-based authentication of digital identitydocuments are disclosed.

In one aspect, embodiments include authentication based on globaltexture information extracted from a digital image representative of anidentity document.

Global texture-based authentication may include generating a globaltexture profile for an identity document image and comparing the globaltexture profile with a stored profile associated with a jurisdictionclass of the identity document.

In embodiments, global texture-based authentication may be configured tobe insensitive to PII and to tolerate wide variations in the ambientillumination captured in the digital image.

In another aspect, embodiments may, additionally or alternatively,include authentication based on local texture information extracted fromthe digital image.

Local texture-based authentication may include generating one or morelocal texture patches representative of texture information of one ormore select local blocks of the identity document. The one or more localtexture patches are provided as input to one or more local detectorseach trained to detect the presence of a forgery based on a targetmanipulation space.

The target manipulation space may be, without limitation, physicalportrait photo substitution, digital image splicing, inpainting,resampling, photocopy recapture, or LCD screen recapture.

In embodiments, the local detectors may each include a texture-basedconvolutional neural network (CNN) classifier; a machine learning-basedclassifier; or an Error Level Analysis (ELA)-based classifier.

Classifiers may be trained using augmented training data designed tosimulate the target manipulation space of the classifier. In anembodiment, a style-transfer based network may be used to synthesizeimages with a particular global document style.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate the present disclosure and, togetherwith the description, further serve to explain the principles of thedisclosure and to enable a person skilled in the pertinent art to makeand use the disclosure.

FIG. 1 illustrates an example authentication system according to anembodiment.

FIG. 2A illustrates an example approach for extracting global textureinformation from a digital image according to an embodiment.

FIG. 2B illustrates another example approach for extracting globaltexture information from an image according to an embodiment.

FIG. 3 illustrates an example approach for extracting local textureinformation according to an embodiment.

FIG. 4 is a flow chart that illustrates an example method for generatinga global texture profile according to an embodiment.

FIG. 5 is an example that illustrates the generation of texturedescriptors according to an embodiment.

FIG. 6A illustrates the synthesis of an example digital imagerepresentative of a manipulated identity document with photo recapturestyle.

FIG. 6B illustrates the synthesis of an example digital imagerepresentative of a manipulated identity document with LCD recapturestyle.

FIG. 7 is a flowchart that illustrates an example process forauthenticating a digital identity document according to an embodiment.

FIG. 8 illustrates an example computer system which may be used toimplement embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates an example authentication system 100 according to anembodiment of the present disclosure. As would be understood by a personof skill in the art based on the teachings herein, system 100 isprovided for the purpose of illustration only and is not limiting ofembodiments of the present disclosure.

As shown in FIG. 1 , system 100 includes a normalization module 104, atexture information generator 108, a jurisdiction class detector 114, agallery indexing module 116, a database 118, a fraud detector 120, and adecision module 134.

In an embodiment, operation of system 100 begins by receiving a digitalimage 102. The digital image 102 represents an identity document (ID),such as a national identity card, a passport, or a driver's license, forexample.

Normalization module 104 may perform various transformations on theimage 102. For example, if the image is in color, the image 102 may betransformed to a grayscale image. Additionally, normalization module 104may flat render the image 102 to remove any content not related to theID represented by the image. For example, content outside of a detectedboundary of the ID may be removed.

The normalized image 106 is then provided to texture informationgenerator 108. In another embodiment, the normalized image 106 may alsobe provided to jurisdiction class detector 114.

Texture information generator 108 is configured to generate, based onthe digital image 106, a global texture profile 126 representative ofglobal background texture information of the ID and/or one or more localtexture patches 124 representative of texture information at one or moreselect local blocks of the ID.

In an embodiment, texture information generator 108 may include atexture block extractor 110 and a global texture profile generator 112.

Texture block extractor 110 may be configured to extract global textureinformation 122 and/or local texture information 124 from the image 106.As would be understood by a person of skill in the art based on theteachings herein, texture information refers to information thatquantifies the perceived texture of an image. The perceived texture is afunction of the spatial variation of brightness intensity in the image.

Global texture information 122 may correspond to texture informationextracted at a plurality of blocks of the image distributed over theentire area of the digital image. Local texture information 124 maycorrespond to texture information extracted at specific local areas ofthe image, for example at or around specific features of the image. Inan embodiment, the local texture information 124 may include one or moresets of texture information (124-1, . . . , 124-n) each tailored toenable the detection of a particular forgery manipulation (manipulationspace) and/or tailored for a specific type of local detector. Each setof texture information 124-1, . . . , 124-n may include one or morelocal texture patches as further described below.

FIG. 2A illustrates an example approach for extracting global textureinformation from a digital image according to an embodiment. As shown inFIG. 2A, the digital image may be partitioned into a plurality of blocks202, according to a defined number of rows and columns. Textureinformation may then be extracted at each of the blocks 202. Theextracted texture information may accordingly capture texture with awider correlation, as illustrated, for example, by texture blocks 204_(1,0) and 204 _(1,1) corresponding respectively to blocks 202 _(1,1)and 202 _(1,2) of the digital image.

In another approach, as shown in FIG. 2B, the global texture informationmay be extracted from smaller, regional blocks 206 distributed over theentire area of the digital image. The extracted texture information mayaccordingly capture regional texture as illustrated, for example, bytexture blocks 208 _(1,1) and 208 _(1,2) corresponding respectively toblocks 206 _(1,1) and 206 _(1,2) of the digital image.

FIG. 3 illustrates an example approach for extracting local textureinformation from a digital image according to an embodiment. Accordingto this example approach, the local texture information includes one ormore local texture patches representative of texture information of oneor more select local blocks of the ID.

In an embodiment, generating the one or more local texture patchescomprises detecting, based on a target manipulation space, one or morefeatures of interest of the ID.

According to embodiments, the target manipulation space can be, withoutlimitation, physical portrait photo substitution, digital imagesplicing, inpainting, resampling, photocopy recapture, or LCD screenrecapture.

As would be understood by a person of skill in the art based on theteachings herein, physical portrait photo substitution refers to thereplacement of the portrait photo on a physical ID; digital splicingrefers to the digital manipulation of an image to add external content(e.g., to change the PII) into the image; inpainting refers to theremoval of content from an image using pixels coming from the sameimage; resampling refers to the use of interpolation to geometricallytransform a digital image or a portion of an image; photocopy recapturerefers to the generation of a digital image by photographing a physicalcopy of a document; and LCD screen recapture refers to the generation ofa digital image by photographing an LCD screen display of a document

For example, in FIG. 3 , the target manipulation space is physicalportrait photo substitution, and the one or more features of interestmay include a portrait photo of the ID. Various feature detectiontechniques may be used to detect features of interest. For example, todetect a portrait photo in an ID, the Hough transform may be used todetect the lines corresponding to the frame of the portrait photo.

After detecting the one or more features of interest in the ID, the oneor more select local blocks of the ID are defined as a function of thedetected features of interest. Subsequently, texture information isextracted at each of the one or more select local blocks to generate theone or more local texture patches. For example, in FIG. 3 , afterdetecting the portrait photo 302, one or more local select blocks 304are defined as rectangular or square blocks straddling the top, bottom,left, and right boundaries of the portrait photo. Texture information isthen extracted from one or more of the select local blocks 304 to obtainthe one or more local texture patches.

In an embodiment, texture block extractor 110 may be configured toautomatically set to black pixel blocks of the digital image thatinclude personally identifiable information (PII), prior to extractingthe respective texture information at each of the plurality of blocks.

Returning to FIG. 1 , texture block extractor 110 may be configured toprovide global texture information 122 to global texture profilegenerator 112. Texture block extractor 110 may also be configured toprovide local texture information 124 to one or more local detectors 132of fraud detector 120.

Global texture profile generator 112 may be configured to generate aglobal texture profile, based on global texture information 122,representative of global background texture information of the ID.

FIG. 4 is a flow chart that illustrates an example process 400 forgenerating a global texture profile according to an embodiment. One ormore steps of process 400 may be performed by texture block extractor110 or by global texture profile generator 112.

As shown in FIG. 4 , process 400 begins in step 402, which includesextracting, based on the digital image, respective texture informationat each of a plurality of blocks distributed over the entire area of thedigital image. Step 402 may be performed by text texture block extractor110, for example.

Next in step 404, the process includes generating a respective texturedescriptor based on the extracted respective texture information foreach of the plurality of blocks. Step 404 may be performed by textureprofile generator 112.

In an embodiment, the texture descriptor generated for each of theplurality of blocks includes one or more of: a histogram-based texturedescriptor, a correlation-type texture descriptor, or a local binarypattern (LBP)-based texture descriptor. The histogram-based texturedescriptor captures statistics of finer local (2^(nd)-order) differencesamong texture pixels. The correlation-type descriptor captures coarserand wider correlation between larger areas (patches) of texture pixels(like moiré patterns). The LBP-based texture descriptor capturesstatistics of relative variations of local intensity of texture pixels.

In an embodiment, the texture descriptor for a given block includes acombination of descriptors of the different types mentioned above. Forexample, the texture profile can include a concatenation of multiplesegments each obtained using a different type of texture descriptor. Theadvantage of combining descriptors of different types to form thetexture descriptor of a given block is that the resulting descriptorconveys a richer characterization of the texture of the given block.

Process 400 terminates in step 406, which includes combining therespective texture descriptors of the plurality of blocks to generatethe global texture profile of the ID. In an embodiment, the combinationof the respective texture descriptors of the plurality of blocksincludes concatenating the respective texture descriptors to obtain theglobal texture profile of the ID.

FIG. 5 is an example that illustrates the generation of texturedescriptors according to an embodiment. As shown, texture information isextracted from a plurality of regional blocks 502 distributed over theentire area of the digital image. The texture information extracted fromthe blocks 502 may be as illustrated by texture blocks 504 _(1,j-1) and504 _(1,j) corresponding respectively to blocks 502 _(1,j-1) and 502_(1,j).

The texture descriptors may be histogram-based as illustrated bydescriptors 506 _(1,j-1) and 506 _(1,j) which correspond respectively totexture blocks 504 _(1,j-1) and 504 _(1,j).

The texture descriptors of the plurality of blocks 502 are combined togenerate a global texture profile of the digital image. In the case ofhistogram-based texture descriptors, the combination includes addingtogether the bins of the histograms to obtain a combined histogram.

Returning to FIG. 1 , jurisdiction class detector 114 may be configuredto detect a jurisdiction class 136 of the identity document based on thedigital image 106. The jurisdiction class of an identity document may bedefined by an issuing authority (e.g., country, state, etc.) and acategory of the identity document (national identity card, passport,driver's license, etc.).

In an embodiment, jurisdiction class detector 114 may be configured toscan the image 106 and to use character recognition techniques toextract information relating to issuing authority and/or category of theidentity document.

Jurisdiction class detector 114 provides the detected jurisdiction class136 to gallery indexing module 116. In an embodiment, gallery indexingmodule 116 queries database 118, using the detected jurisdiction class136, to retrieve a stored texture profile 128 associated with thedetected jurisdiction class 136. In an embodiment, the stored textureprofile 128 corresponds to a previously generated global texture profilefor the detected jurisdiction class 136.

The previously generated global texture profile for a jurisdiction classmay be generated based on a gallery image for the jurisdiction class andis referred to hereinafter as a gallery profile. The gallery image for ajurisdiction class is an image of an identity document that is known tobe authentic for the jurisdiction class.

In an embodiment, the gallery profile for a jurisdiction class may begenerated in the same manner as global texture profile 126, namely usingnormalization module 104, texture block extractor 110, and globaltexture profile generator 112. The gallery profile is then provided togallery indexing module 116, which indexes and stores the galleryprofile into database 118 based on its associated jurisdiction classdetected by jurisdiction class detector 114.

According to embodiments, gallery profiles for a multitude ofjurisdiction classes may be generated and stored in database 118 priorto live authentication using system 100.

Fraud detector 120 may be configured to detect the presence of a forgeryin the digital image based on the global texture profile 126 or the oneor more local texture patches 124. According to embodiments, thedetected forgery may belong to one or more manipulation spaces, such asphysical portrait photo substitution, digital image splicing,inpainting, resampling, photocopy recapture, or LCD screen recapture. Inan embodiment, fraud detector 120 may include a global detector 130 andone or more local detectors 132.

Global detector 130 may be configured to detect forgeries based onglobal texture profile 126. In an embodiment, global detector 130compares the global texture profile 126 to the stored texture profile128 associated with the detected jurisdiction class of the identitydocument represented by the digital image 106.

Based on the comparison, the global detector 130 and/or a decisionmodule 134 may identify the digital image 106 as a recaptured image froma photocopy or a computer screen, or as having an incorrect backgroundlayout for the jurisdiction class of the identity document, for example.

In an embodiment, two texture profiles are compared by measuring adifference for each type of descriptor inside the profile. Thedescriptor differences are then merged to obtain a final texture profilescore. In an embodiment, the descriptor differences are calculated asdistance metrics. For a histogram-based descriptor, the distance metricmay be the intersection of two histograms (one from the global textureprofile 126, the other from the stored texture profile 128). In anembodiment, a dropout policy to avoid high-noise bins may be employed.For a correlation-type descriptor, the distance metric may be a scoremapping with a threshold to ensure that an aliasing signal of the globaltexture profile 126 is not larger than that of the stored textureprofile 128.

In an embodiment, the global detector 130 may be configured to account,in the comparison, for ambient illumination variations between thedigital image 106 and the gallery image used to generate the galleryprofile 128. In an embodiment, this is made possible by ensuring thatthe descriptors used in the texture profile consist of only relativeinformation, such as differences (or gradients), correlationcoefficients, and statistics of intensity variations and gradients.

The setting to black of PII areas in the generation of the profiles asdescribed above allows the profile comparison to be PII insensitive.

Local detectors 132-1, . . . , 132-n may each be configured to detectforgeries based on respective local texture patches 124. In anembodiment, each detector 132-i is configured to receive respective oneor more local patches 124-i tailored for the specific manipulation spacethat the detector 132-i is intended to detect.

According to embodiments, local detectors 132-1, . . . , 132-n may beconfigured to detect, for example, one or more of: physical portraitphoto substitution, digital splicing, inpainting, resampling, photocopyrecapture, or LCD screen recapture.

In an embodiment, a local detector 132-i configured to detect physicalportrait photo substitution may be configured to receive one or morelocal textures patches as illustrated in the example of FIG. 3 describedabove. In a similar manner, a local detector configured 132-j configuredto detect digital splicing of PII may be configured to receive one ormore local patches extracted at or in the neighbourhood of the PII ofinterest on the ID. On the other hand, a local detector 132-k configuredto detect photocopy recapture or LCD screen recapture may require onlyrandomly extracted local texture patches.

Local detectors 132-1, . . . , 132-n may each be implemented in variousways. Without limitation, local detectors 132-1, . . . , 132-n may eachinclude a texture-based convolutional neural network (CNN) classifier; amachine learning-based classifier; or an Error Level Analysis(ELA)-based classifier.

For the purpose of illustration and not limitation, a texture-based CNNclassifier may be implemented as described in “Andrearczyk et al., Usingfilter banks in convolutional neural networks for textureclassification. Pattern Recognition Letters, 84, 63-69, 2016,” which isincorporated herein by reference in its entirety. As would be understoodby a person of skill in the art, the implementation would be modified asneeded to address the particular problem of the present disclosure.

For the purpose of illustration and not limitation, a machinelearning-based classifier may be implemented as a Support Vector Machine(SVM) as described in “Armi et al., Texture Image Analysis and TextureClassification Methods—a Review. International Online Journal of ImageProcessing and Pattern Recognition, Vol. 2, No. 1, pp. 1-29, 2019,”which is incorporated herein by reference in its entirety. As would beunderstood by a person of skill in the art, the implementation would bemodified as needed to address the particular problem of the presentdisclosure.

For the purpose of illustration and not limitation, an ELA-basedclassifier is a classifier that acts on an ELA signal obtained from theimage, rather than on the raw image. The ELA signal includes errorlevels computed at one or more pixels of the image. Typically, the errorlevels are computed by compressing the image at a known error rate andthen by taking the difference pixel-by-pixel between the original imageand the compressed image. Pixels with error levels above a definedrelative threshold may be identified as having been subject tomanipulation.

In an embodiment, rather than using an absolute threshold, the ELA-basedclassifier may use a relative threshold for different regions of theimage. For example, the error levels over the non-portrait photo regionof the image may be used to estimate a dynamic and relative thresholdfor the error levels over the portrait photo region of the image for theclassification.

In an embodiment, to improve classification performance, classifiers maybe trained using augmented training data designed to simulate the targetmanipulation space of the classifier. Data augmentation is generallyrelevant to all types of classifiers, and especially for CNNclassifiers.

In an embodiment, the augmented training data may be generated using astyle-transfer based network as described in “Gatys et al., A neuralalgorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015),”which is incorporated herein by reference in its entirety. Such networkmay be configured to generate images that mix the content of a firstimage with the style of a second image. As would be understood by aperson of skill in the art, the implementation would be modified asneeded to address the particular problem of the present disclosure.

For example, FIG. 6A illustrates the synthesis of example digital imagesrepresentative of a manipulated identity document with photo recapturestyle. Specifically, image 602 represents the image which content is tobe reproduced in the synthesized image (the content image), and image604 represents the image which style (photo recapture) is to betransferred into the synthesized image (the style image).

Images 606 and 608 represent respectively coarse-detail and fine-detailsynthesized images resulting from mixing content image 602 and styleimage 604 using a style-transfer based network.

Similarly, FIG. 6B illustrates the synthesis of a coarse-detail image612 and of a fine-detail image 614 by mixing content image 602 with astyle image 610 having an LCD recapture style, using a style-transferbased network. It is noted that style image 610 may have an entirelydifferent content than content image 602.

As shown in FIGS. 6A and 6B, the mixing of content and style imagesaccording to this approach results in the synthesized images having aparticular global document style. As such, data augmentation may includenot only pixel-level geometric, photometric, and noise-addingoperations, but also texture-level data augmentation to generatefraudulent training images with a wider range of pixel correlation orwith a periodic texture structure.

Returning to FIG. 1 , decision module 134 may be configured to receivethe outputs of each of global detector 130 and local detectors 132-1, .. . , 132-n. Based on the received outputs, decision module 134 may beconfigured to determine whether the image 102 has been manipulated orrepresents a fraudulent identity document.

FIG. 7 is a flowchart that illustrates an example process 700 forauthenticating a digital identity document according to an embodiment.As would be understood by a person of skill in the art based on theteachings herein, process 700 is provided for the purpose ofillustration only and is not limiting of embodiments of the presentdisclosure.

As shown in FIG. 7 , process 700 begins in step 702, which includesgenerating, based on a digital image representative of an ID, a globaltexture profile representative of global texture information of the ID.The global texture profile may be generated as described above withreference to FIGS. 1, 2A-2B, 4, and 5 .

Before, after, or concurrently with step 702, in step 704, the processmay include detecting a jurisdiction class of the ID based on thedigital image. In an embodiment, step 704 may be performed by a detectorsuch as jurisdiction class detector 114.

Next, step 706 includes retrieving, from a database, a stored textureprofile associated with the detected jurisdiction class of the ID. In anembodiment, the stored template corresponds to a previously generatedglobal texture profile for the detected jurisdiction class. Thepreviously generated global texture profile for a jurisdiction class maybe generated based on a gallery image for the jurisdiction class.

Subsequently, step 708 includes comparing the global texture profile toa stored texture profile associated with the jurisdiction class of theidentity document. In an embodiment, the comparison is made PIIinsensitive by removing the PII from the digital images beforegenerating the global texture profile and/or the stored texture profile.In another embodiment, the comparison is made less sensitive to ambientillumination variations between the digital image and the gallery imageby the choice of descriptors that form the texture profile. In anembodiment, this is made possible by ensuring that the descriptors usedin the texture profile consist of only relative information, such asdifferences (or gradients), correlation coefficients, and statistics ofintensity variations and gradients.

If, in step 708, the global texture profile does not match the storedtexture profile, process 700 proceeds to step 710, which includesidentifying the presence of a forgery in the digital image. For example,step 710 may include identifying the digital image as a recaptured imagefrom a photocopy or a computer screen. Alternatively or additionally,step 710 may include identifying the digital image as having anincorrect background layout for the jurisdiction class of the identitydocument.

Otherwise, if, in step 708, the global texture profile matches thestored texture profile, process 700 transitions to step 712, whichincludes generating the one or more local texture patches based on thedigital image. The one or more local texture patches may be generated asdescribed above with reference to FIGS. 1 and 3 based on one or moretarget manipulation spaces. In an embodiment, the target manipulationspaces may include physical portrait photo substitution, digital imagesplicing, inpainting, resampling, photocopy recapture, or screenrecapture.

Process 700 then proceeds to step 714, which includes detecting thepresence of a forgery based on the one or more local texture patches. Inan embodiment, the detection may be performed as described above usinglocal detectors each configured and trained to detect a specific type offorgery.

FIG. 8 illustrates a computer system 800 which may be used to implementembodiments of the present invention. According to an embodiment, theabove-described authentication system 100 may be implemented usingcomputer system 800.

As shown in FIG. 8 , computer system 800 includes a processor 802 and amemory 804. A computer program (PROG) may be stored on memory 804. Thecomputer program may include instructions that, when executed by theprocessor 802, cause the processor 802 to execute a method forauthenticating a digital image representative of an identity documentaccording to any of the embodiments described herein.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of embodiments of the present disclosure shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

1. A method for authenticating a digital image representative of anidentity document (ID), comprising: generating, based on the digitalimage, at least one of: a global texture profile representative ofglobal background texture information of the ID or one or more localtexture patches representative of texture information of one or moreselect local blocks of the ID; and detecting the presence of a forgeryin the digital image based on the global texture profile or the one ormore local texture patches.
 2. The method of claim 1, wherein generatingthe global texture profile comprises: extracting, based on the digitalimage, respective texture information at each of a plurality of blocksdistributed over the entire area of the digital image; generating arespective texture descriptor based on the extracted respective textureinformation for each of the plurality of blocks; and combining therespective texture descriptors of the plurality of blocks to generatethe global texture profile of the ID.
 3. The method of claim 2,comprising: setting to black pixel blocks of the digital imagecomprising personally identifiable information (PII) prior to extractingthe respective texture information at each of the plurality of blocks.4. The method of claim 2, wherein the respective texture descriptorincludes one or more of: a histogram-based texture descriptor, acorrelation-type texture descriptor, or a local binary pattern(LBP)-based texture descriptor.
 5. The method of claim 1, wherein theglobal texture profile is generated based on the digital image, andwherein detecting the presence of a forgery based on the global textureprofile comprises: detecting a jurisdiction class of the identitydocument based on the digital image; and comparing the global textureprofile to a stored texture profile associated with the jurisdictionclass of the identity document.
 6. The method of claim 5, wherein saidcomparing comprises accounting for ambient illumination variationsbetween the digital image and a gallery image used to generate thestored texture profile.
 7. The method of claim 5, wherein the globaltexture profile does not match the stored texture profile, and whereindetecting the presence of a forgery based on the global texture profilecomprises: identifying the digital image as a recaptured image from aphotocopy or a computer screen; or identifying the digital image ashaving an incorrect background layout for the jurisdiction class of theidentity document.
 8. The method of claim 5, wherein the global textureprofile matches the stored texture profile, the method furthercomprising: generating the one or more local texture patches based onthe digital image; and detecting the presence of a forgery based on theone or more local texture patches.
 9. The method of claim 1, whereingenerating the one or more local texture patches comprises: detecting,for a target manipulation space, one or more features of interest of theID; defining the one or more select local blocks of the ID based on thedetected one or more features of interest; and extracting, based on thedigital image, texture information at each of the one or more selectlocal blocks to generate the one or more local texture patches.
 10. Themethod of claim 9, wherein the target manipulation space comprisesphysical portrait photo substitution, digital image splicing,inpainting, resampling, photocopy recapture, or screen recapture. 11.The method of claim 9, wherein the target manipulation space is physicalportrait photo substitution, wherein the one or more features ofinterest comprise a portrait photo of the ID, and wherein the one ormore select local blocks comprise one or more blocks straddling aboundary of the portrait photo.
 12. The method of claim 9, whereindetecting the presence of a forgery based on the one or more localtexture patches comprises: providing the one or more local texturepatches as input to a local detector trained to detect the presence ofthe forgery for the target manipulation space.
 13. The method of claim12, wherein the local detector comprises one or more of: a texture-basedconvolutional neural network (CNN) classifier; a machine learning-basedclassifier; or an Error Level Analysis (ELA)-based classifier.
 14. Themethod of claim 1, comprising: flat-rendering the digital image prior togenerating the global texture profile or the one or more local texturepatches.
 15. A system for authenticating a digital image representativeof an identity document (ID), comprising: a processor; and a memorystoring instructions that, when executed by the processor, cause theprocessor to implement: a texture information generator configured togenerate, based on the digital image, at least one of: a global textureprofile representative of global background texture information of theID or one or more local texture patches representative of textureinformation of one or more select local blocks of the ID; and a detectorconfigured to detect the presence of a forgery in the digital imagebased on the global texture profile or the one or more local texturepatches.
 16. The system of claim 15, wherein the detector comprises aglobal detector configured to: detect a jurisdiction class of theidentity document based on the digital image; and compare the globaltexture profile to a stored texture profile associated with thejurisdiction class of the identity document.
 17. The system of claim 15,wherein the detector comprises a local detector configured to detect,based on the one or more local texture patches, the presence of theforgery for a target manipulation space.
 18. The method of claim 17,wherein the local detector comprises one or more of: a texture-basedconvolutional neural network (CNN) classifier; a machine learning-basedclassifier; or an Error Level Analysis (ELA)-based classifier.
 19. Themethod of claim 18, wherein the ELA-based classifier employs a dynamicand target-relative threshold for detecting pixel manipulation.
 20. Anon-transitory computer useable medium having stored thereoninstructions that, when executed by a processor, cause the processor toperform a method for authenticating a digital image representative of anidentity document (ID), the method comprising: generating, based on thedigital image, at least one of: a global texture profile representativeof global background texture information of the ID or one or more localtexture patches representative of texture information of one or moreselect local blocks of the ID; and detecting the presence of a forgeryin the digital image based on the global texture profile or the one ormore local texture patches.